(80 days)
The VNUS Closure™ System is indicated for use in the coagulation of blood vessels during general surgery.
The VNUS Closure™ System consists of three main components: The VNUS Closure Probe, the VNUS RF Generator and the VNUS Instrument Cable. The Closure Probe is provided Sterile, and is a single-use, disposable device. The RF Generator is non-sterile. The Instrument Cable is autoclave sterilized by the user. An optional Footswitch for RF ON/RF OFF is provided for use at the physician's discretion.
The RF Generator is a high frequency electronic, bipolar, software controlled instrument. It allows the user to set Power, Temperature and Time values, and provides user displays of Power. Temperature and Time (setpoints and measured values) as well as measured impedance and other messages. The RF Generator works in a temperature controlled, power limited manner, based on operator settings and temperature feedback provided by a thermocouple in the Closure Probe.
The Closure Probe is used to carry RF energy to the desired treatment site and provide temperature feedback to the RF Generator. It is designed to deliver the RF energy in a bipolar manner.
The Instrument Cable is used to connect the Closure Probe to the RF Generator.
The provided document is a 510(k) premarket notification for the VNUS Closure System. It focuses on demonstrating substantial equivalence to predicate devices rather than conducting a detailed performance study with acceptance criteria and specific statistical hypothesis testing as might be found in a PMA (Premarket Approval) submission or a dedicated clinical trial report for an AI/ML device.
Therefore, much of the requested information regarding "acceptance criteria," "study proving device meets acceptance criteria," "sample sizes," "expert ground truth," "adjudication methods," "MRMC studies," "standalone performance," and "training set details" cannot be found within this document. These studies are typically performed for new or significantly modified devices, or for AI/ML devices where performance claims against a defined ground truth need to be established.
The document states:
- "Non-clinical tests performed by VNUS have demonstrated the substantially equivalent performance of the Closure System with predicate electrosurgery systems used for substantially equivalent indications."
- "Based upon the design, materials, function, intended use, comparison with currently marketed devices and the non-ciinical testing performed by VNUS, it is concluded that the Closure System is substantially equivalent to the noted predicate devices in safety and effectiveness."
This indicates that non-clinical testing (bench testing, perhaps animal studies) was conducted to demonstrate equivalence, but the specific details of "acceptance criteria" and how they were "proven" in a quantitative sense are not disclosed here. The basis for approval is substantial equivalence to existing devices, not a direct demonstration of meeting detailed performance metrics in human studies.
Given these limitations, here is what can be extracted and what cannot:
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not specified. | "Demonstrated substantially equivalent performance" to predicate devices based on non-clinical tests. |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Sample Size (Test Set): Not specified. The document refers to "non-clinical tests," which typically involve bench testing and possibly animal models, not human subject test sets with specific sample sizes as would be relevant for AI/ML performance.
- Data Provenance: Not specified. "Non-clinical tests performed by VNUS" suggests internal testing.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
- Number of Experts: Not applicable/not specified. This type of expert assessment for ground truth is typically relevant for medical imaging or diagnostic AI/ML devices, not for an electrosurgery system described in this 510(k). The "ground truth" for this device would likely be based on physical measurements and functional performance in a laboratory setting.
- Qualifications of Experts: Not applicable/not specified.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Adjudication Method: Not applicable/not specified, as there is no mention of expert ground truth establishment for a test set in the context of this device and submission type.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance
- MRMC Study: No, an MRMC study was not done, and is not applicable to an electrosurgical device as described here. This type of study is specifically designed for evaluating diagnostic devices, especially those involving human interpretation of medical images or data (e.g., AI in radiology).
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Standalone Performance: Not applicable. An electrosurgical system is inherently a device used with human intervention. The concept of "standalone performance" as typically applied to an AI algorithm without human input doesn't fit this device. The system has automated features (temperature control, power limiting), but these are integral to its operation in human hands.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- Type of Ground Truth: Not explicitly stated in terms of common medical imaging 'ground truth' types. For non-clinical tests of an electrosurgical device, ground truth would relate to physical parameters and outcomes of energy delivery (e.g., precise temperature readings, coagulation efficacy in tissue models, impedance measurements, safety parameters like absence of unintended tissue damage).
8. The sample size for the training set
- Sample Size (Training Set): Not applicable/not specified. This is not an AI/ML device in the modern sense that would require a "training set" for model development. The system's software controls are likely based on engineering principles and embedded logic, not statistical machine learning from data.
9. How the ground truth for the training set was established
- Ground Truth (Training Set): Not applicable/not specified, as there is no mention of a training set for an AI/ML model.
§ 878.4400 Electrosurgical cutting and coagulation device and accessories.
(a)
Identification. An electrosurgical cutting and coagulation device and accessories is a device intended to remove tissue and control bleeding by use of high-frequency electrical current.(b)
Classification. Class II.